GitLab

* Try remove eager scripting calls

* remove script call
Co-authored-by: eellison <eellison@fb.com>
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

- xenial -> bionic
- fixed warnings: added os and replaced matrix by jobs

* [WIP] Add Tensor implementation for pad

* Unified Pad and F.pad opertion for PIL and Tensor inputs

* Added another test and improved docstring

* Updates according to the review

* Cosmetics and replaced f-string by "".format

* Updated docstring
- added compatibility support for padding as [value, ] for functional_pil.pad
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

Signed-off-by: Eli Uriegas <eliuriegas@fb.com>

* Update docs

* Update docs/source/ops.rst
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

* Add Deformable Convolution in Doc

* Add PSRoIPool and PSRoIAlign in Doc

Refactoring to use contexts managers, list comprehensions when more idiomatic, and minor renaming to help reader clarity (#2335)

* Refactoring to use contexts managers, list comprehensions when more idiomatic, and minor renaming to help reader clarity.

* Fix flake8 warning in video_utils.py

Fix: PyAV does not support floating point numbers with decimals as FPS when writing and will throw in case this constraint is not satisfied. (#2334)

I was reading through the code and noticed a few spelling errors
orignal -> original
maintaing -> maintaining

* Pin ninja version for Windows jobs

* Update build_wheel.sh

* Pin for all

* add convert_image_dtype to functionals

* add ConvertImageDtype transform

* add test

* remove underscores from numbers since they are not compatible with python<3.6

* address review comments 1/3

* fix torch.bool

* use torch.iinfo in test

* fix flake8

* remove double conversion

* fix flake9

* bug fix

* add error messages to test

* disable torch.float16 and torch.half for now

* add docstring

* add test for consistency

* move nested function to top

* test in CI

* dirty progress

* add int to int and cleanup

* lint
Co-authored-by: Philip Meier <meier.philip@posteo.de>

* Adjust hue

* Adjust hue acceps torch.tensor uint8
Co-authored-by: Vikram Mukunda Rao Tankasali <vikramtankasali@devvm765.lla0.facebook.com>

* feat: torchscriptable adjusments

* fix: tensor output type

* feat: ColorJitter torchscriptable

* fix: too many blank lines

* fix: documentation spacing and torchscript annotation

* refactor: list type for _check_input

* refactor: reverting to original syntax
Co-authored-by: clement.joudet <clement.joudet@inventia.life>

Summary:
Allow writes of >= 2^32 bytes. High-res video can cross this threshold sometimes.
LHS is `size_t`, but RHS is all `int32`, and will overflow for output tensors >2Gb.

Reviewed By: jsawruk

Differential Revision: D21255664

fbshipit-source-id: 7b4c5da989777297a89e73615aaeee8c7a13186a
Co-authored-by: Tilak Sharma <tilaksharma@fb.com>

* Make RandomHorizontalFlip torchscriptable

* Make _is_tensor_a_torch_image more generic

* Make RandomVerticalFlip torchscriptable (#2283)

* Make RandomVerticalFlip torchscriptable

* Fix lint

* Bugfix in pad

* Address review comments

* Fix lint

Summary:

We've made two recent changes to QAT in PyTorch core:
1. add support for SyncBatchNorm
2. make eager mode QAT prepare scripts respect device affinity

This PR updates the torchvision QAT reference script to take
advantage of both of these.  This should be landed after
https://github.com/pytorch/pytorch/pull/39337 (the last PT
fix) to avoid compatibility issues.

Test Plan:

```
python -m torch.distributed.launch
  --nproc_per_node 8
  --use_env
  references/classification/train_quantization.py
  --data-path {imagenet1k_subset}
  --output-dir {tmp}
  --sync-bn
```

Reviewers:

Subscribers:

Tasks:

Tags:

* Add more tests to NMS

* Fix lint

* Remove interpolate in favor of PyTorch's implementation

* Bugfix

* Bugfix

* Fix binary builds with CUDA 9.2 on Windows

* Remove packages according to VC_YEAR

* get pts directly instead of storing full frames to get pts later

* fix linting

* add initial pts value
sort pts

* catch decoding errors for read_video_timestamp

* Add norm_layer to MobileNetV2

* Add simple test case

* Small fix

Co-authored-by: eellison <eellison@fb.com>

* add compatibility table to README

* Update README.rst

* Update README.rst
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

* Avoid `using` in header files

* Fix clang_format

* use clang-format-7 to reformat code

* add layer freezing param to maskrcnn_resnet50_fpn

* freeze ayer param in keypointrcnn_resnet50_fpn

* layer freeze tests for mask and keypoint rcnn

* correct linting errors

* correct linting errors.

* correct linting errors

* Fix #2221, DenseNet issue with gradient checkpoints (memory_efficient=True)

* Add grad/param count test for mem_efficient densenet

* Deprecate Conv2d, ConvTranspose2d and BatchNorm

* Fix lint

* Update README.md

added some clarity to get the examples executable.  Waiting to hear back if instructions should mention to setup COCO dataset

* Update README.md

* Update README.md

* Update README.md

* Update README.md

* Update README.md